AI Financial Agent 🤖

This is a proof of conncept AI financial agent. The goal of this project is to explore the use of AI for investment research. This project is for educational purposes only and is not intended for real trading or investment.

👋 Demo: You can use a live demo of this project here.

Disclaimer

This project is for educational and research purposes only.

• Not intended for real trading or investment
• No warranties or guarantees provided
• Past performance does not indicate future results
• Creator assumes no liability for financial losses
• Consult a financial advisor for investment decisions

By using this software, you agree to use it solely for learning purposes.

Table of Contents 📖

• Features
• Setup
• Usage
• Financial Data
• Deploy Your Own

Features 🌟

• AI SDK
  • Unified API for generating text, structured objects, and tool calls with LLMs
  • Hooks for building dynamic chat and generative user interfaces
  • Supports OpenAI (default), Anthropic, Cohere, and other model providers
• Financial Datasets API
  • Access to real-time and historical stock market data
  • Data is optimized for AI financial agents
  • 30+ years of financial data with 100% market coverage
  • Documentation available here
• shadcn/ui
  • Styling with Tailwind CSS
  • Component primitives from Radix UI for accessibility and flexibility

Setup 🛠️

1. Set up your environment variables:

# Create .env file for your API keys
cp .env.example .env

Set the API keys in the .env file:

# Get your OpenAI API key from https://platform.openai.com/
OPENAI_API_KEY=your-openai-api-key

# Get your Financial Datasets API key from https://financialdatasets.ai/
FINANCIAL_DATASETS_API_KEY=your-financial-datasets-api-key

# Get your LangSmith API key from https://smith.langchain.com/
LANGCHAIN_API_KEY=your-langsmith-api-key
LANGCHAIN_TRACING_V2=true
LANGCHAIN_PROJECT=ai-financial-agent

Note: You should not commit your .env file or it will expose secrets that will allow others to control access to your various OpenAI and authentication provider accounts.

2. Next, to deploy your own version of the AI Financial Agent in production, you need to link your local instance with your Vercel and GitHub accounts. For this exercise, we need the Postgres database.

3. Install Vercel CLI: npm i -g vercel

4. Link local instance with Vercel and GitHub accounts (creates .vercel directory): vercel link

5. Download your environment variables: vercel env pull

6. Complete your .env file

Usage 🎮

After completing the steps above, simply run the following command to start the Docker development server:

docker-compose up -d

Your app template should now be running on localhost:3001.

Financial Data

This template uses the Financial Datasets API as the financial data provider. The Financial Datasets API is specifically designed for AI financial agents and LLMs.

The Financial Datasets API provides real-time and historical stock market data and covers 100% of the US market over the past 30 years.

Data includes financial statements, stock prices, options data, insider trades, institutional ownership, and much more. You can learn more about the API via the documentation here.

Note: Data is free for AAPL, GOOGL, MSFT, NVDA, and TSLA.

If you do not want to use the Financial Datasets API, you can easily switch to another data provider by modifying a few lines of code.